Feature importance methods:
Also relevant to our work are techniques that measure feature-importance. These works generally focus on understanding why a model made a particular prediction. To this end, [1]} introduce LIME, a local explanation method that approximates a linear explanation model around a given example. Later, [2]} proposed DeepLift specifically for deep net models. [3]} introduced SHapley Additive exPlanations (SHAP), a method to estimate feature importance using an approximation of the features Shapley values. LIME assumes that the local model is linear, while SHAP does not have any such assumptions. [3]} show a connection between DeepLIFT and Shapely values introduced as `Deep SHAP.' Each of those techniques concentrates on the importance of features, whereas we focus on the importance of a modality as a whole. [5]} proposed to assess how much a classifier benefits from interactions across different data modalities. To do this, EMAP approximates a multi-modal classifier with classifiers that each depend on only a single data modality. In contrast, we measure the perceptiveness of each modality separately and for every data point independently, i.e., we aim to identify how much a classifier relies on each data modality.
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